10 Open-Weight LLM Architectures in Early 2026

🔑 Enhanced Key Takeaways

• •Arcee AI's Trinity series features a flagship 400B parameter MoE model with 13B active parameters, alongside smaller Trinity Mini (26B/3B active) and Trinity Nano (6B/1B active) variants, accompanied by a detailed technical report on GitHub and arXiv[1].
• •Qwen3-Coder-Next (80B total, 3B active) employs a Gated DeltaNet + Gated Attention hybrid, enabling 262k native context length and outperforming larger models like DeepSeek V3.2 on coding benchmarks[1][3].
• •OpenAI's gpt-oss-120b (117B total, 5.1B active MoE) is their first open-weight release since GPT-2, matching o4-mini on benchmarks like AIME and MMLU while supporting commercial use and safety alignments[2][3][4].
• •Epoch AI research shows open-weight models now lag proprietary SOTA by only three months on average, a sharp reduction from prior years[2][4].

🛠️ Technical Deep Dive

• •Qwen3-Next (80B-A3B): Hybrid MoE with 512 experts (10 active, ~3B active params), Gated DeltaNet + Gated Attention hybrid, 262k native context, multi-token prediction (MTP) training[1][3].
• •Arcee Trinity Large: 400B total MoE, 13B active parameters[1].
• •gpt-oss-120b: 117B total MoE, 5.1B active parameters, runs on single 80GB GPU, supports safety evaluations and guard models[2][3][4].
• •Qwen3 Next prior (235B-A22B): High expert count with shared expert, 262k native context via YaRN scaling[1].

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline